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Feature Set Transformations

It is a well known phenomenon that the success of learning depends on the selection input representation. In many cases only a transformation of this representation into another space allows the learning of the inherent patterns. Examples for these transformations are:
  • Feature selection: the selection of a feature subset prevents noisy or redundant features from covering the patterns which should be learned.
  • Feature construction: the construction of new features from given ones allows the explicit representation of feature interactions. Learning methods can make use of this knowledge.
  • Feature extraction: the calculation of features from complex data structures like time series of multidimensional data (e.g. images) is called feature extraction.

Projects

SFB 475 subproject A4
SFB 531 Computational Intelligence

Software

RapidMiner (YALE)

Staff

Klinkenberg, Ralf
Mierswa, Ingo
Pölitz, Christian

Past Master Thesis

Publications

Kopcke, Hanna. . Fachbereich Informatik, Universitat Dortmund, 2003.
Mierswa, Ingo and Wurst, Michael. . No. CI-194/05, 2005.
Mierswa, Ingo and Morik, Katharina. . No. 55/04, 2004.
Mierswa, Ingo and Morik, Katharina. . In Informatik Spektrum, Themenheft Musik, Vol. 28, No. 5, pages 381--388, 2005.
Mierswa, Ingo and Morik, Katharina. . In Machine Learning Journal, Vol. 58, pages 127--149, 2005.
Mierswa, Ingo. . Fachbereich Informatik, Universit\"at Dortmund, 2004.
Ritthoff, Oliver and Klinkenberg, Ralf and Fischer, Simon and Mierswa, Ingo. . No. CI-127/02, Dortmund, Germany, 2002.
GECCO '06: Proceedings of the 8th annual conference on Genetic and evolutionary computation Mierswa, Ingo and Wurst, Michael. . In GECCO '06: Proceedings of the 8th annual conference on Genetic and evolutionary computation, pages 1545--1552, New York, NY, USA, ACM Press, 2006.
Proc. of the International Conference on Machine Learning, Workshop on Meta Learning Mierswa, Ingo and Wurst, Michael. . In Proc. of the International Conference on Machine Learning, Workshop on Meta Learning, 2005.
Proceedings of the European Conference on Machine Learning (ECML 2005) Mierswa, Ingo and Wurst, Michael. . In Proceedings of the European Conference on Machine Learning (ECML 2005), pages 641--648, Berlin, Springer, 2005.
Proceedings of the Genetic and Evolutionary Computation Conference GECCO 2005, Workshop on Self-Organization In Representations For Evolutionary Algorithms: Building complexity from simplicity Mierswa, Ingo and Morik, Katharina. . In Proceedings of the Genetic and Evolutionary Computation Conference GECCO 2005, Workshop on Self-Organization In Representations For Evolutionary Algorithms: Building complexity from simplicity, pages 293--300, New York, NY, USA, ACM, 2005.
Classification -- the Ubiquitous Challenge, Proc. of the 28. Annual Conference of the GfKl 2004 Mierswa, Ingo. . In Classification -- the Ubiquitous Challenge, Proc. of the 28. Annual Conference of the GfKl 2004, pages 600--607, Springer, 2004.
Proc. of LWA 2004 - Lernen - Wissensentdeckung - Adaptivitat Mierswa, Ingo. . In Proc. of LWA 2004 - Lernen - Wissensentdeckung - Adaptivitat, 2004.
LLWA 03 - Tagungsband der GI-Workshop-Woche Lernen - Lehren - Wissen - Adaptivitat Mierswa, Ingo. . In LLWA 03 - Tagungsband der GI-Workshop-Woche Lernen - Lehren - Wissen - Adaptivitat, 2003.
Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2003) - Part II Ritthoff, Oliver and Klinkenberg, Ralf. . In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2003) - Part II, pages 1606--1607, Springer, 2003.
Proceedings of the 2002 U.K. Workshop on Computational Intelligence (UKCI-02) Ritthoff, Oliver and Klinkenberg, Ralf and Fischer, Simon and Mierswa, Ingo. . In Proceedings of the 2002 U.K. Workshop on Computational Intelligence (UKCI-02), pages 147--154, Birmingham, UK, University of Birmingham, 2002.